Package: condor-doc Source: condor Version: 7.8.8~dfsg.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6118 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.8~dfsg.1-2~nd+1_all.deb Size: 1460438 SHA256: 4b7503e591c48830a7197a36f6546bc34915282f2d412b165ea56b42aa1effb6 SHA1: 1dd3703cca13aa81b5033ae022e3841160630c37 MD5sum: 5284c0bc8774fb42be55c08bf276c485 Description: distributed workload management system - documentation Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: connectomeviewer Version: 2.1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1578 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd+1_all.deb Size: 1356156 SHA256: 434aff9b028c4333df4aff71cc45e6b82a98574f6297ddab70d0ebc260ff5e6a SHA1: 5dc49f902c6d89fd0fea7758ce53c9462ec73db4 MD5sum: a9b946a201ad29742748d1c152b6fd57 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: dh-systemd Source: init-system-helpers Version: 1.18~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd+1_all.deb Size: 13820 SHA256: 5c6c86d4863c6322a9e6512918f65d3621106a79bb50d159df73068dc4f82efb SHA1: 99fb38f6bfb0c38a01dd2e304f1c32ffbe0c0b2b MD5sum: 7a19fa0768f1c1d627df931054cb945b Description: debhelper add-on to handle systemd unit files dh-systemd provides a debhelper sequence addon named 'systemd' and the dh_systemd_enable/dh_systemd_start commands. . The dh_systemd_enable command adds the appropriate code to the postinst, prerm and postrm maint scripts to properly enable/disable systemd service files. The dh_systemd_start command deals with start/stop/restart on upgrades for systemd-only service files. Package: fail2ban Version: 0.8.13-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 563 Depends: neurodebian-popularity-contest, python:any (>= 2.6.6-7~), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.13-1~nd+1_all.deb Size: 165572 SHA256: 53a9841c100622d30e9c0d01f55316f59c4ff35a432c6be2f4788f4469e25b14 SHA1: 51c0d062796cd6fda68fdb0d0e1aa061f77443cd MD5sum: fdd74431e51539a99f2d81356aa77cf6 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: freeipmi Version: 1.1.5-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd+1_all.deb Size: 928 SHA256: 05f29073e2746666e0fbc4c111150ef67a4ea5a91974f3d43fb41e2bb16e60ff SHA1: 54dbd1897168ea82b4c803ac9cdf3185b1e1c24f MD5sum: 7a7f53cc7987a2216d7d16bf3b5f0ca9 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This meta-package depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.1.5-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd+1_all.deb Size: 296942 SHA256: f1e328eeb5e8aa8cde083ce4c3b1e115f911921a7165f6719cc109685e5fa76f SHA1: 368c7818e51c28f1d98d426a4e687df0b70a0d32 MD5sum: 1b586f2ac96616824bb815a6b8addf7f Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: fslview-doc Source: fslview Version: 4.0.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd+1_all.deb Size: 2346520 SHA256: 00ec15002eb3332a7725e068e08040b8f6ee213a5865c40d8048b1d61ee0ad31 SHA1: 8f573d168f4be5999da86816126368b160207ea4 MD5sum: 23e9540f3d9063363c90cc184e4c9037 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gmsl Version: 1.1.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd+1_all.deb Size: 13860 SHA256: d5e113a0261e40edce8fb498d2bb7852c648002247fffdff0452381a4b2ecdef SHA1: 11595f3dcf608a93147968af5da25409ce67f80e MD5sum: 1f3da7118ebb92d891e0eca8f578fe12 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Source: guacamole-client Version: 0.8.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 476 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd+1_all.deb Size: 426802 SHA256: 30425f953711295f9fd13c922f1f6cdfb967eae6d2b35805872681de6cab0984 SHA1: 10dc2e2a857d30f9a48b314928809fa24c950efb MD5sum: 146eaa1ce187ce8df480c722af1b3657 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole-client Version: 0.8.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd+1_all.deb Size: 6738 SHA256: 35e27bb61847fd126702dfd459303228bb781ce180fa78b663d7263479f5b2a6 SHA1: 97eec3f03694256ce8d38f1ae5db05b2e6de793a MD5sum: abb7483920ef5ea1c5619fd31786a892 Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: impressive Version: 0.10.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 331 Depends: neurodebian-popularity-contest, python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2) Recommends: pdftk, perl Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.5-1~nd+1_all.deb Size: 151670 SHA256: adbb7a44a2e7137b4e424d028a52ad32076841c809112d99100b73ba3d03c7bc SHA1: a3431337bdb81b0111a77accb88a898870082466 MD5sum: 7ce7984d6e349bc9e708773248c1d369 Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd+1_all.deb Size: 9644 SHA256: 0d13ef08a008124bb9da089c6b0ee0b6786334ccc1f455d0fbf23dc513dd40df SHA1: bc8cb6cadf98cc14994f98441238f49353e3a04c MD5sum: 4ea2b3f0bbadd9c29c191cb08ac94709 Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: init-system-helpers Version: 1.18~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd+1_all.deb Size: 13476 SHA256: d19f7f7a8cc30eebf191c3e5ad052e7bed02fdc1193cd7d909b96fcb70fb0a92 SHA1: 030b394a1bb44ed637be9f28365d434d5b7b76b9 MD5sum: 1082547d50241a858f76eaea6a830965 Description: helper tools for all init systems This package contains helper tools that are necessary for switching between the various init systems that Debian contains (e.g. sysvinit, upstart, systemd). An example is deb-systemd-helper, a script that enables systemd unit files without depending on a running systemd. . While this package is maintained by pkg-systemd-maintainers, it is NOT specific to systemd at all. Maintainers of other init systems are welcome to include their helpers in this package. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.2.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2677 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.2.1-2~nd+1_all.deb Size: 2408052 SHA256: 87a7a9d2d23a62eb09d7bc3cee5fe711315750b90c2fbd0a56904fb1c8eb4044 SHA1: 739a8da4cc45306113be264af825dc75ca93a8d1 MD5sum: 24cc64153345b88410e1979e04bfd7c8 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4808 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd+1_all.deb Size: 1306542 SHA256: 33fc418d5aa20d8ed5764ba27113cf8b7dfd6e161f925ce1b3bf179bf11fb31c SHA1: 39a91cecc912b7b453902b3746d62849a55e0b52 MD5sum: 9f74e872ca8b460a4350a340d804f98b Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16672 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd+1_all.deb Size: 7241256 SHA256: a0d2235483d3300b6213c473d783db7e62487249a9bed0d418dbad6d44693be8 SHA1: 947c3033758d7974461bd0758e0d765af1750dc6 MD5sum: c52d4bb47c6d7375400b74e24c2f9fd8 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd+1_all.deb Size: 900 SHA256: 610ebc3a7bcc05bd450b367852ab26dad5c2f8b668e000fc73880a07cdffda2f SHA1: c722a2a679e2931d56dc828366f160165f16d9d0 MD5sum: 9e7976ee869362eaa11884d2e1ff00c0 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd+1_all.deb Size: 828 SHA256: 1f0ce5d07095b2529a2f935f8715d834215d6c4f016179fc33ba6b11dd5855f7 SHA1: e2194703b6e2426cbb732cc34070157910c6c0ca MD5sum: fe3fa702999665bd8751b4daf6afb0d2 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd+1_all.deb Size: 914 SHA256: ed093f1f0751c34cedd9910b094deecb52e3a7d8865119c5de7d47996acdea7b SHA1: 4aebbc9ff1ce980725e419f8754294d3ea472dc6 MD5sum: 96a49d95cc8ce5bff8422fb5d3f378ba Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython1x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 3842882 SHA256: 7c1441bc77ba7c7fd3bdbe815f7c7f7f6463fa9ef0313a9e93609cdfe179dd8c SHA1: 4caf2b8e43102cc073fa5d48befbdea5f0bd2f59 MD5sum: 47491a39015e2f209b730ad0cb5ada5d Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10389 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 3694410 SHA256: 791e5bf6963a169984fc99ea7876edcf8ee4d9d395d324c12dda2aef4f0a602a SHA1: 94c9c4fed79527354a553fd0e0a794eb18c92ae6 MD5sum: 4a3eea4d61a721ca8acfda4d8c57ef61 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 962 SHA256: fe4b4c9a3c3c0f4a0a6fea5b8988cfdf1116d52d69e2c1a0fee034193a621b73 SHA1: 7fff71aa3955c95a06d37dc68325299f9d5304ef MD5sum: cd9e1c312a5b0daaa52b9744d53c19d0 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 888 SHA256: b92af7fb89a64885bac32001ff8af087e44e8b5b7d2ff631255d786fe8a80d01 SHA1: aaa3c95e540ffc04b95c335b8059fa1d82c1da4f MD5sum: 8ffb02a51001d8c55e7f68aad8820104 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 972 SHA256: 899ea9da63a3f655e738b2853dc87c805ce75a92303b2f1baf93e31c9aa5d743 SHA1: b8d030862023fbf54da8e753ce0b86d306982254 MD5sum: 6db341ac45a0e710a04f62bd052e45c6 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython2x Version: 2.0.0+git8-gee204ae-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12337 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7), python (<< 2.8) Recommends: python-tornado (>= 3.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib, environment-modules Suggests: ipython2x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython2x/ipython2x_2.0.0+git8-gee204ae-1~nd+1_all.deb Size: 5617136 SHA256: 8bd356916406ac6cd32400a0f84f7c961c575e597f1cb27359d8fc3557311155 SHA1: f8ff90471d165b6b6f5364285f5943c8c56b8112 MD5sum: d3ea479e159c72a987a2fcb370b51985 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython 2.x seres with all fresh goodness from the IPython team. It provides IPython2X module thus not conflicting with system-wide installed IPython Package: ipython2x-doc Source: ipython2x Version: 2.0.0+git8-gee204ae-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12949 Depends: neurodebian-popularity-contest, libjs-jquery, ipython2x (= 2.0.0+git8-gee204ae-1~nd+1) Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython2x/ipython2x-doc_2.0.0+git8-gee204ae-1~nd+1_all.deb Size: 4678124 SHA256: b5c017712653a30fa8a62aaa8601c3b9146f3c63184419ef17121da49c38e864 SHA1: 571824bad0b488d73fdc42eac1a4fe70bb10d9f6 MD5sum: 855a4ce18a4ed06cef96e0796756cdd6 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython 2.x. It provides IPython2X module thus not conflicting with system-wide installed IPython Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd+1_all.deb Size: 90816 SHA256: 6f15ee9429e9a77208075aa20ac716d28777bb6ae56b6d928d2394f8eba532f5 SHA1: f983624268cf3366f85733d527b08deb8581e0be MD5sum: 812834b439f4a80838af58f2d5ecacc4 Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14011 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd+1_all.deb Size: 837904 SHA256: 6efc28d4222b4fb91ba00745e720dcd6ae7588b802cf7b12051065e2f784bcd0 SHA1: e36b5e877dfe57819cadb4ca52228fa321a74390 MD5sum: de045c0e2212f343fde74be4c895c334 Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libmialm-doc Source: libmialm Version: 1.0.7-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233 Depends: neurodebian-popularity-contest Suggests: devhelp Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/libm/libmialm/libmialm-doc_1.0.7-2~nd+1_all.deb Size: 21290 SHA256: b0dd34278ad9a52597e66191c62a74baff467c13c2c870179302634477b9c8fa SHA1: 838394dd07ce9586d20f35968c02f61bac4c766f MD5sum: 7eefb55e9ae26db08d0750f3da3ec2a7 Description: Documentation for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the library documentation. Package: libopenwalnut1-doc Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48075 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd+1_all.deb Size: 2681636 SHA256: d042340fe7dcca76e8158aa438e6b3e15ff18b784521652317a66f70e98c397e SHA1: 3b9b8845f6836b1497663e58556a9c2ed9497b4c MD5sum: d3f9e8e06482d3b6b8bca3c09e3737cd Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: matlab-support-dev Source: matlab-support Version: 0.0.19~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.19~nd+1_all.deb Size: 7214 SHA256: d4d5ca7dc4bfd09a284a4ed1ea966794e87f6641586f54cb291dee692f6d814f SHA1: b34327f74bc8d05af20a16ec6eb2f848f52513db MD5sum: e3a2a5ca34fb4be3f17c2b16b7d846f8 Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mia-tools-doc Source: mia Version: 2.0.13-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1145 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd+1_all.deb Size: 78990 SHA256: 51558554d8205e9835e53d3e5a38f2f9da5222d20da8798e81788580b9265918 SHA1: 356c818a63c03035944b985e1419040aaa7a2278 MD5sum: c9a4f6051dec225ca8244a32e5feb682 Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mricron-data Source: mricron Version: 0.20130828.1~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1713 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20130828.1~dfsg.1-1~nd+1_all.deb Size: 1661750 SHA256: daff90caebd29239222d3ec9cf7270ef5b3c99c61fcd93a2c34e0f01c24360b6 SHA1: 43eea6479b0069b5d3126467c026051cbf087e83 MD5sum: 31360abe2ee1a79da127abf1532cd81c Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20130828.1~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1022 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20130828.1~dfsg.1-1~nd+1_all.deb Size: 579946 SHA256: 34f49e0381a18e74d63c72753b3f8fdcd55e9c3ff6da25627c52b348b9dfa371 SHA1: 512de9b057517ff8940fab1de0b7e66eb1384cfe MD5sum: c15648a80d1095f2cd15dc34fad05d89 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.12-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3528 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.12-1~nd+1_all.deb Size: 3199776 SHA256: 4abb82cb1f240754d3bead18288426bdd8862b186cbca4ace358b42c97a1dfe7 SHA1: b0e995bc80a61c78c328a8a3d226cb35155ebd31 MD5sum: 6e421c1e6eb30afffc6a98884e70112e Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: netselect-apt Source: netselect Version: 0.3.ds1-25~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd+1_all.deb Size: 16790 SHA256: 97280acc29ad04928a2171d14f076325e227c153af82af6002a68cc2f04cdc97 SHA1: d5262a5b9d1465c562d40e782a920ff0b19c6c0e MD5sum: 22f92cf44ff49488ceeed27c77ee0d4e Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.34~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: python, wget, neurodebian-archive-keyring, debconf (>= 0.5) | debconf-2.0 Recommends: netselect Suggests: neurodebian-desktop, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian_0.34~nd+1_all.deb Size: 20584 SHA256: d5f4d629d80b35df758d429100063754feff9ea1864906d30f62d5f65a9235b2 SHA1: 4509984ee598f10d9f951d644a8d5f9c8d0a674f MD5sum: b6c076547309674c531f0e9fb5ad5e8d Description: turnkey platform for the neuroscience The NeuroDebian project integrates and maintain a variety of neuroscience-oriented (such as AFNI, FSL, PsychoPy, etc.) and many generic computational (such as condor, pandas, etc.) software projects within Debian. . This package enables NeuroDebian repository on top of the stock Debian or Ubuntu system. Package: neurodebian-archive-keyring Source: neurodebian Version: 0.34~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Breaks: neurodebian-keyring (<< 0.34~) Replaces: neurodebian-keyring (<< 0.34~) Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-archive-keyring_0.34~nd+1_all.deb Size: 9138 SHA256: 57f0fe8513ce3c25f374b3fef9c3c60e316c062dacc811907f6a7b6620808572 SHA1: 97111a45e6b1c1d98a8b3f5957019a529647ec26 MD5sum: ffb95d2e91957201f7ec975e87a061de Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-desktop Source: neurodebian Version: 0.34~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 146 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.34~nd+1_all.deb Size: 114764 SHA256: 3491e088372acd617057407db09fb90d50592acde679599e128177c096e710ac SHA1: 083a3bc4d68ffa983f22e3bf4374e8ca8565d8e0 MD5sum: b097c54cb5a6a991fec560fe3ad86a12 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.34~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 81 Depends: devscripts, cowbuilder, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.34~nd+1_all.deb Size: 30956 SHA256: c8abb0f62c5445cdcd12fcd181cda367ebe75644eb7e6c933ebc7ad67b02b573 SHA1: be687bfe6e596ea79a776b2eaca80f0a7b5c37ab MD5sum: c3859fa003c7a5380d71599659b253eb Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.32~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.32~nd+1_all.deb Size: 14030 SHA256: a7b1c22382ef38bcb02197d3f88ef51f8e1f2f5152ad7c18e6d040b6803d3c9f SHA1: 0b8bca805ed756dadec92b4e76d3701ed9b400c6 MD5sum: 6bd47ac8ce904b7e445aca38e8d806a8 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.32~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.32~nd+1_all.deb Size: 7418 SHA256: 6a6686a0d7186992aeeb0520359ca34c15210792010af928b382b5f4caa888a7 SHA1: 89cec44d84d971babd0be50534f2560073246957 MD5sum: 778a5b4f7b7cd1933cfb8808beedb823 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.34~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.34~nd+1_all.deb Size: 11294 SHA256: 6d0cab71fc1ccd1c01b5bcda46b2351bcb9ebd8b3e7ef04ff65c7068ca565bab SHA1: 70909c007e3d067d7135b032f225d5fb2cac76cc MD5sum: 77219d6f3c0c719547a68f9e97192ee3 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.8-1~nd+1_all.deb Size: 615466 SHA256: dced59fedd8784c22d74a0009744aba6c7807afa51e7ac9e192ed451f61355e6 SHA1: 22ee236c39983f6c0d702b28ff2f169a71304d5d MD5sum: 0107ec545637401fd93e4b257c49cac4 Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.5.4+ds-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2216 Depends: neurodebian-popularity-contest, g++-4.9 | g++-4.8 | g++-4.7 | g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python:any (>= 2.6.6-7~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.4+ds-1~nd+1_all.deb Size: 509162 SHA256: 5b2bf5c4fd5838b6a566ec65517b358fb94c96841bd289be80dbbe700d8751aa SHA1: e6e6ac183f5b3282e937ae90519e2b56d550b3c9 MD5sum: 37e350148bba29282bc89f8e572a2b55 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: opensesame Version: 0.27.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.4-2~nd+1_all.deb Size: 24612138 SHA256: b223ca6a17d69da2863cd4ddba9b499d0a3b8b554862fe6623ec17c63eab74a4 SHA1: bf7885235942fade9f1256a1968979918212a7bf MD5sum: daca1a779d091bd428ceb9f90818b04d Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd+1_all.deb Size: 2024448 SHA256: 577686111aba7c2eafbe4c25ab1052d26958bd64471319884a324b9825c07d16 SHA1: 2ab042fb217b2ffec6f55b478cf1e8405786832a MD5sum: 8d7802d259052ef28a0dd2febd7fa4e9 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. Package: packaging-tutorial Version: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.79.00+git16-g30c9343.dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12186 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.79.00+git16-g30c9343.dfsg-1~nd+1_all.deb Size: 5533978 SHA256: 787bd5ad84dbe73357bdee64cbf8bb84ba7c0addc240da20e2566f9567bd7fd5 SHA1: 080fb67606a4efd4984504ced2ae36436adf8967 MD5sum: 2bc81b11b54d7865e062266f4bed50a3 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20140816.dfsg1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58460 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20140816.dfsg1-1~nd+1_all.deb Size: 19246844 SHA256: ad2dd7311a15e345e85bc30830f7580347ab22976d35a1c55e156364129234da SHA1: 02907175fb34dd144ccc52451e13fee9e47fc419 MD5sum: 0620d368b626c1b00d76cabff6c59022 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: python-brian Source: brian Version: 1.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.1-1~nd+1_all.deb Size: 549162 SHA256: c51f0470d85262e3557539b32304455d5bb809a9b1ddac52613abe40a24a7957 SHA1: 7ec1c4157a18c0015dd7365552bd05c66291b9e6 MD5sum: 7ce8090a3b2209003d5808ec167651b2 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6808 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.1-1~nd+1_all.deb Size: 2246624 SHA256: 38ef682cec0640e71c12d12333e41042744720d47394f2a0dc23f45538b8a74a SHA1: 026b4455f186e745bafe42f6b2bc2ec4452acfc5 MD5sum: 96cd6c1886d42aaf452772f7a8fc3dd7 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-dicom Source: pydicom Version: 0.9.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1814 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.8-1~nd+1_all.deb Size: 358934 SHA256: 8bf08f3593cd78318be46c2ddaf988cc65bf31af49c5ef6a114f9e66688531cd SHA1: 26374eee80db5420a45e42bbe8f669509e6b357e MD5sum: dc259e7b0a61765f59253845838c6e3d Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.7.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2954 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.7.1-2~nd+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.7.1-2~nd+1_all.deb Size: 1772278 SHA256: 072f33f2ef31aa1182807c912b58bf0d007f5225f768b0bed4de24dd517e0003 SHA1: 70f1b5b7158d174228024525817cb2b52c9eaf4e MD5sum: d6f30e2b4d9d3098a6d6005bb5d8ea88 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.7.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9458 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: optional Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.7.1-2~nd+1_all.deb Size: 6750052 SHA256: 8ffd86059f7ee8824cf03275e4b34267cb261dd0b1a9276f4b2c063c68645a3f SHA1: 9015b9b1eb645aed8fa63c177213f9bbd8d266c8 MD5sum: 2f8823e1d1dfb2569da0b4f55ee1ad7b Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2419 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-support (>= 0.90.0), python-pygame (>= 1.9.1~), python-opengl (>= 3.0.0), ttf-freefont, libjs-jquery, libjs-underscore Recommends: python-serial (>= 2.5~), python-numpy (>= 1.3.0~) Suggests: python-parallel (>= 0.2), python-pyxid Homepage: http://www.expyriment.org Priority: optional Section: science Filename: pool/main/p/python-expyriment/python-expyriment_0.7.0+git34-g55a4e7e-3~nd+1_all.deb Size: 698616 SHA256: 0b51538d13146d68e561a4e41f6434c5d2da0f595efe67c50f01a4e534ef8aaf SHA1: dac42ac93d31516c2d67b397da1446658d3238ab MD5sum: baf8ddfb4a408c0e43865522241da0e7 Description: Python library for cognitive and neuroscientific experiments Expyriment is a light-weight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments. The major goal is to provide a well-structured Python library for a script-based experiment development with a high priority on the readability of the resulting programme code. Due to the availability of an Android runtime environment, Expyriment is also suitable for the development of experiments running on tablet PCs or smart-phones. Package: python-joblib Source: joblib Version: 0.8.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.8.3-1~nd+1_all.deb Size: 64170 SHA256: 36d490db2f7c2a533ce1dac0fb6ec197712b80028ad58ba5fb921a6ed0d309a1 SHA1: e2e4c684fc5fdc4b5ea235c70db4d38384f8be83 MD5sum: fa184ba8d2e7389859912620e77af0f2 Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: . - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. . This package contains the Python 2 version. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd+1_all.deb Size: 7328 SHA256: dbb35c5dc374c7bc62e95a56d3a14314105025852a66ba61f2472e4ea5b8be65 SHA1: a7bdc4dd42a3963a810fe0c0e73e4ad7ed6a7995 MD5sum: 1d204a47646dc6ed4152895171c87bc9 Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1528 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd+1_all.deb Size: 484096 SHA256: d82dbf8a5b5738ebdf22bd138714552c8332ed81a3d3ce7a15e63e96f203b214 SHA1: 8577b8829def8e006d3c14b6b4011669c9140d4f MD5sum: 41627718c5a1f5de89c8ce8e12c404b8 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mne Version: 0.8.4+dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7181 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-d3, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.8.4+dfsg-1~nd+1_all.deb Size: 4022800 SHA256: ee2546bbbf153c5b09f054cb2b8182495aca868d200ec3e90147e4526a8f771d SHA1: b31e766789ff0948e67d18147c5af1a9679c7268 MD5sum: e6e4e5678d7b8d0b76c74507a155ae0e Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 1.3.1+hg20131106-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 256 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd+1_all.deb Size: 52396 SHA256: 7666b857e080b91eee14cce28b96f3b06c5989971594bfa4bab51dd2df2464d6 SHA1: 942ceb004987cff1e99b2068119b87d142715f5e MD5sum: 827a006faaba6237d23781fad09c591b Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa, python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd+1_all.deb Size: 2205002 SHA256: 41465c88b5c5d855bb5cfb183ef31b621031eb691ba5a8f3ac481bec2fe61bd8 SHA1: 40e31da97e30b6c2af3f28dfcd4b255560f765e2 MD5sum: b36ff1ec87893ae209624c75e8934b87 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37565 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd+1_all.deb Size: 8454400 SHA256: 9f730cbbc6fdcfce45ecca5ef036d74ea074eaedf2b4105fde7baf0028f11350 SHA1: 4510a24072100ffb1d4220f2d66d21abde733b9d MD5sum: 32c7629e7f9e01d9f7ca4d2c621b85be Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa2 Source: pymvpa2 Version: 2.3.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6458 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.3.1-1~nd+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev, python-pprocess Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.3.1-1~nd+1_all.deb Size: 3685634 SHA256: d2812501ebed0231fa4bb15c1a6fb1340ad91ea856d82a7dc0f362c9ef6b7dc1 SHA1: 64d43a995633d197b565bd110739fda58b952139 MD5sum: 0c90de1adba7e65578cd7e69e43599f8 Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.3.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27518 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.3.1-1~nd+1_all.deb Size: 4483146 SHA256: d921766d81999d661c1db0e4fbc0e662cf25253935547ec3abc855560949d240 SHA1: 425409856378ebfed0d1c954bef0c3bfe5cd0f73 MD5sum: e46237c18fc3d68e8f6e8cd552d8d7b7 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.3.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2909 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.3-1~nd+1_all.deb Size: 1378610 SHA256: 268bcbe349cdd59dcd6c81b69f509e39abc526f812bd5f07208932a109266e6b SHA1: be5f458f69e1f2108a65756ef29c1a542be0bbe1 MD5sum: 5da5190a50f6ef3282f67c895329fb02 Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-neuroshare-doc Source: python-neuroshare Version: 0.9.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 284 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://www.g-node.org/neuroshare-tools Priority: extra Section: doc Filename: pool/main/p/python-neuroshare/python-neuroshare-doc_0.9.2-1~nd+1_all.deb Size: 95620 SHA256: 4b008b4e512ecc425d792b05a5f237701cff8f0b10847f9ae4cefbe836b52f12 SHA1: 0c55225790940875eff3b0dc7ac1454c7dea4230 MD5sum: acccb1a0fb534f3233a73ff94d58d8a9 Description: Python interface and tools for Neuroshare The Neuroshare API is a standardized interface to access electrophysiology data stored in various different file formats. To do so, it uses format- specific shared libraries. . This package provides a high-level Python interface to the Neuroshare API that focuses on convenience for the user and enables access to all available metadata and data. The data is returned in NumPy arrays, which provides a quick route to further examination and analysis. . In addition, this package contains the ns2hdf converter tool that converts neuroshare-compatible files into the HDF5 (Hierarchical Data Format, ver. 5) file format. . This package contains HTML documentation files for python-neuroshare. Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd+1_all.deb Size: 32506 SHA256: 1b7a6109b4cd73ca4ed17d0f33010df1d73c8bfcdd469311c48d58714fd99755 SHA1: 101a6b891d69881db9e5bd6bfc176aaa2de3ca28 MD5sum: 191a7178983f856cb47820d478a3a791 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4159 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel, python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd+1_all.deb Size: 1826550 SHA256: da75dca6c3f18abbcf2002e7d6f9431cdc0a637a5ed16bb8c6cb9f6e5618b4ef SHA1: 85f6cbb660c7dabcde1a35f1da2cd017a9c91001 MD5sum: 9da5139ccc2bf80df0c39c79d31d39d8 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Python-Version: 2.6, 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2446 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd+1_all.deb Size: 448186 SHA256: ed6dcae3cb79e05b8c1b336ce4eb2dc5479288c1aa69c1c321a69033a22f2ae3 SHA1: 719894fd05fe60017dad91079d1e6d972f9a398e MD5sum: 84ba334286af8bf241c85739d659e6f0 Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nipy Source: nipy Version: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2865 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0-1~nd+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.6-nipy, python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0-1~nd+1_all.deb Size: 785860 SHA256: f02f5b7e0d62168b495c371ddbf87b8378f7f67bc60b70297a9aa78d353e8500 SHA1: 9311f209de048e3167e7f99a8ec2653a2a943197 MD5sum: bddf885165aea270af36d814965100dc Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.6, 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10296 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0-1~nd+1_all.deb Size: 3763100 SHA256: 91c3278a29832c96a59450fa4f51e8ff9087e5cf96a1c5955071a1a6a89a12de SHA1: f372bc014b24ae5140698255435856002cbfa91f MD5sum: e9dc281c91fe803e10306abc73713484 Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.9.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3521 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.9.2-1~nd+1_all.deb Size: 579968 SHA256: 184a65c0581c57417dbeab5a574a492d092ec30031d8a0cd3940b415d49a4c4d SHA1: c1edd3e7bb372859bbd7959f4013c3b9660695e2 MD5sum: 334bcab011155b258ca39a9acda1cfaf Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.9.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16516 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.9.2-1~nd+1_all.deb Size: 6052492 SHA256: 59fbca3792302ebf2217b809602977695247b00825ccc9f8df5eb7bb219966eb SHA1: cd3d0a1671f88eb954d31422005c5e677e1a64ed MD5sum: 1aed38c2b8332b1ae8b3b0c66d644a68 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9348 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.5-1~nd+1_all.deb Size: 2541310 SHA256: af9e69254c7e2502adfea0501ad85b26da3fc10d33e8f51b8b353cdc66259698 SHA1: c0394b3a4ecdb6eae4fc448393167d93951c1310 MD5sum: d0ec12c2f52e045094c70f115b6c2ac8 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7731 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.5-1~nd+1_all.deb Size: 5758768 SHA256: c8f8d7a7bad8c81d7576246452ff0d726fa7832b478769c72cf119cad1646673 SHA1: 924a3ee615cfef4ba8bba2b57757f4ae52cca995 MD5sum: c488d46e7ada9cf3b61bccc157e9c1eb Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd+1_all.deb Size: 245088 SHA256: 720267e7fc1297916d72081d7bffedfc4e911f4cba267f9e83f65ee6cf7eac3b SHA1: 2a31c5c6bad612fa5d880b23d6c2c2628c1aef20 MD5sum: 5ffcdd148bf0a2e648d7c3960953fc20 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6, 2.7 Package: python-openpyxl Source: openpyxl Version: 1.7.0+ds1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 453 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose, python-pil, python-imaging Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.7.0+ds1-1~nd+1_all.deb Size: 75956 SHA256: c2c543413c82617ba5d0257ddd0573d99d3934ca1c8922ee057897ab487e8a11 SHA1: 7fe6c99383f297ebae67c4dd7be9987d68cc7f92 MD5sum: 0d4ae1955f19c6f216857202cf1c095e Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pandas Source: pandas Version: 0.14.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8918 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.14.1-1~nd+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.14.1-1~nd+1_all.deb Size: 1251638 SHA256: d7907eb5f8360dc34bc8e979c4410c13d66c26941eaeb1a49028f0f4ad79d144 SHA1: 1f8bef4176c3adede121b19d5c596f7e20809018 MD5sum: 1b1351b2ff378e05997eefe9fd15bd21 Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version. Package: python-patsy Source: patsy Version: 0.3.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 720 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.3.0-3~nd+1_all.deb Size: 162722 SHA256: 796dae438afe9079742f5dc07eac00b049e4921794475322fee3118d46e78bd0 SHA1: d5dd43feeb9542da64d6f3997ac62a238275fffe MD5sum: 826a5d8bf0ad6d051e6a4950c7c72820 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.3.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1313 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.3.0-3~nd+1_all.deb Size: 355888 SHA256: 63666d8b608ea797fb33f45e50980dca469ec806a4a51876cef31ca492a74ffb SHA1: 2900b7f3d7137043229827e9bfc96249fda9ef50 MD5sum: a563f8ce2cfe5237a7c05d81dcb9470d Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd+1_all.deb Size: 34266 SHA256: 6ef3aa699e927edfc8ec788d98beac10bec4de47387f92d79d44b0183b3c3c3d SHA1: d8942b2e7dddc5e29cc52b2a4cca69a392348a09 MD5sum: 36530320f9038b882b0e8b9d5be61505 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.6, 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd+1_all.deb Size: 81598 SHA256: 3842060cc266ddff9a61fb88f4c4e34004ad6e6aba6ba60d74ce046dc8c1b126 SHA1: 36cc022ca9cf83a947a9c1a3c7eabffa69dea1f2 MD5sum: 23b94adc9313ef79112c781728008a69 Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.7 Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd+1_all.deb Size: 21334 SHA256: 3ab479e9d42286158d724eb219d6205e3c8071a2a8fd6436afc501b57ecf086b SHA1: 19b81597aeb2806a30580c43b1fcf5f5ad3d586d MD5sum: 662336ec73a1d4c272a6d2763ef118df Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd+1_all.deb Size: 819288 SHA256: 7eb7da90f6e629e4f1eb828beac597cd0d9950fd05a06f9bb135d2e58d6a2d13 SHA1: 6fd9ebae91ca87f9a3efa61b64ccb01de6b033f7 MD5sum: b608bf8ddef20a65f3bef3f99781ed82 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pymc-doc Source: pymc Version: 2.2+ds-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1840 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.2+ds-1~nd+1_all.deb Size: 903860 SHA256: 49c91294d31287e3ca2606e0cc3bef7182e9276130bd6d2805e5edd03c91e6ea SHA1: 48be75f9daca7b5d4277fdd5c48382aee76c7113 MD5sum: 4d31a5b8b73523612b42ce1cb1d9c0f4 Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd+1_all.deb Size: 192126 SHA256: 4c25bbb4a6efbe9c9614c69977d255840ceb5469e917e9e42631963e11fa73b5 SHA1: 4d68ad72d900c06b6d482eaabab6847de44296f2 MD5sum: bb6a423667c3716a6baee943a4d73d50 Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1722 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd+1_all.deb Size: 376516 SHA256: c44e63035749ea2429ed9f2aab12e3ce41aa533de51f0cf3ce9836f882e3a477 SHA1: 2baec2a6b0c311f22f1a6e685cea20ae7019dac7 MD5sum: 603bfa4501f120f7ca01ed5ed95293b0 Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd+1_all.deb Size: 62610 SHA256: 24764ab44e8e2357cdb8d4882acce352d96b34ed6b3af8be217617eb51848f83 SHA1: 9367905e8af4cb696831c327b20e43a3e2d52616 MD5sum: d08b442a214c35f1e1f9fa595d311cf6 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits-learn Source: scikit-learn Version: 0.15.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 44 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.15.2-2~nd+1_all.deb Size: 40840 SHA256: 1b6dff4bbbf375b17c48403ee29aade1f687d4561370bef168692a76fa6e4b11 SHA1: c98670cd2d651403fd2bacd7af875ce43affdb0b MD5sum: 9984422c441ed30267499c145eba9830 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.5.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.5.0-1~nd+1_all.deb Size: 5304 SHA256: 29cf2328b5f13a0e03000a81a21d4a116b058601adf0cfb36ebe417828d3943e SHA1: dbbc6a909ea89f452bc9cee2bbe1757fd7ef3b2d MD5sum: 15876f8e64368cb2fa400daae8703204 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-seaborn Source: seaborn Version: 0.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 383 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.4.0-1~nd+1_all.deb Size: 74864 SHA256: 15726c2ca248b480df787ffe1075749698787c0216be6345ea9d04c6af264332 SHA1: 499c6f428952360d12ebf63cbfd9103fcb033988 MD5sum: d75bca8a0b3085763bcbfc67edbaabbb Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 2 version of the package. Package: python-skimage Source: skimage Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15134 Depends: neurodebian-popularity-contest, libfreeimage3, python-numpy, python-scipy (>= 0.10), python-six (>= 1.3.0), python-skimage-lib (>= 0.10.1-1~nd+1), python (>= 2.7), python (<< 2.8) Recommends: python-imaging, python-matplotlib (>= 1.0), python-nose, python-pil, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.10.1-1~nd+1_all.deb Size: 11929000 SHA256: 26a15e982952e9b25c3a01429758b7c907961ec356cca9752891db15ad8908aa SHA1: 08847ca24011d9e0f6628a01f29f0133981f7482 MD5sum: f526a0e7d0fb0fbd9aaf45f64a046ca5 Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21906 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.10.1-1~nd+1_all.deb Size: 17242376 SHA256: d2a4a015f0485af34b6b6f65092b9f09dc0cfa7176ba05d8679bbb0f91ed2ce4 SHA1: c47549b8d38b9b2e79035324d81121e63b6c344c MD5sum: 2abdd9cf6de3cb46714cb1a422bc3221 Description: Documentation and examples for scikit-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.15.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3969 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.15.2-2~nd+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.15.2-2~nd+1_all.deb Size: 1012010 SHA256: 2e7b239ecd167500aebf096715ed2fbc6f15b1fa47cdd99881163ac47f2f868d SHA1: 5bd4dc43a3f01c837678255d7ef67eac25062079 MD5sum: 9a57ab505c0d0aaa93e8c0e079c1c562 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Package: python-sklearn-doc Source: scikit-learn Version: 0.15.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 66916 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.15.2-2~nd+1_all.deb Size: 42648916 SHA256: 83eae7f19836a07efcb06a5f4b49a860a541e65ec6c9aa766e0900b0321d8bcd SHA1: d517236f909ea9603beac04d36bc5ebd7770590e MD5sum: 2f506274f6c7c3980f47f021186b3bc1 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4028 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd+1_all.deb Size: 1651710 SHA256: a848e076d4c5edb65fb283f745212616fcf9a34e60d9b1a8856567a394ba1107 SHA1: 50297650b3e49c95185c7857e668e4e21c720064 MD5sum: eabe7544210191b0f23c9a80a9065e86 Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2020 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.4.1-1~nd+1_all.deb Size: 307812 SHA256: d1df3800edd796c8b4228300182035b6e5be40808175a0be9c3bed4a02f75617 SHA1: 2bbd42d0f6d1c9c2d48730def9793e2b58d44ad3 MD5sum: a0cfdeacec5a0b79782bfeb3b0a892bb Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.5.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20472 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.5.0-1~nd+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.5.0-1~nd+1_all.deb Size: 3436046 SHA256: 6a74d457b92d7e2ae08e5a4082b18715052f1a5f075f9dff09bc6c70007534a5 SHA1: c8eb47001722bbd7bc01c5c3bf17e0dc6d206a55 MD5sum: 291cc81d2b6524b6dc4ea5737ecbcad4 Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.5.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32480 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.5.0-1~nd+1_all.deb Size: 7033708 SHA256: a6753e11b89023eb026afef51377e59eb75236f22265cdffed8440111f85597a SHA1: 7237729ad692bc21727a7db03bb4df11342235b9 MD5sum: 5ec8456810dfeadf8fad964c8c32db36 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd+1_all.deb Size: 28904 SHA256: bf545c8b3b5ece2227156e364694d07e7df8b5c2a98cc662c61077e70539e87d SHA1: 096d69ae640c7a9dbdbd958e260da4745308ac9e MD5sum: e49aa156e4c8f1ab8f34630c44d82bbb Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.6, 2.7 Package: python3-joblib Source: joblib Version: 0.8.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 250 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-nose, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.8.3-1~nd+1_all.deb Size: 61316 SHA256: d84fa180f8b49d272588cb0941ff07b2dbff49c070fee5e24101e37ca68b9dd5 SHA1: a16a932509a812fb12a6a87217d6f3a477a46502 MD5sum: 427adb68be03a697e20666df3a2e3e4c Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: . - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. . This package contains the Python 3 version. Package: python3-pandas Source: pandas Version: 0.14.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8903 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.6~), python3:any (>= 3.3.2-2~), python3-dateutil, python3-tz, python3-pandas-lib (>= 0.14.1-1~nd+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.14.1-1~nd+1_all.deb Size: 1249336 SHA256: b41633b1703ba07e9b173da98bca0d910ffa4d36f092ef0b4acbca5d359870f5 SHA1: 3d52e16d13ddb9ed3530d0db68d5ddce6d5576f4 MD5sum: 209fd97f8397bbb70e2867319898f090 Description: data structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.3.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 717 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.3.0-3~nd+1_all.deb Size: 162206 SHA256: 80509997c2f3cedf14602e3d16bb9b44d6237aa9be9966e53b00bccb087a3b6b SHA1: 68853f4bee8deecb446a2aa12d3ebbabc048a8a8 MD5sum: c82dfa389861e2b7cab1be727408ac90 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-seaborn Source: seaborn Version: 0.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 383 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-pandas, python3-matplotlib Recommends: python3-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python3-seaborn_0.4.0-1~nd+1_all.deb Size: 74948 SHA256: cccd20ad29f74e77255ccb7fa56b785fe589031b6e55eb52bb36daea51df689a SHA1: 4eab0eb95474cfdf51602a9c6b9f810d1a18334c MD5sum: dde9c38dae7c8e8937d853bc4d1e4f34 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 3 version of the package. Package: python3-skimage Source: skimage Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15115 Depends: neurodebian-popularity-contest, libfreeimage3, python3-numpy, python3-scipy (>= 0.10), python3-six (>= 1.3.0), python3-skimage-lib (>= 0.10.1-1~nd+1), python3:any (>= 3.3.2-2~) Recommends: python3-imaging, python3-matplotlib (>= 1.0), python3-nose, python3-pil Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.10.1-1~nd+1_all.deb Size: 11915866 SHA256: cd53b6ec9524fc904a752a41f025002dff4be06130bd005bef04287cfcd8cf84 SHA1: 158f4ec9919b03f9c936cfa6f281c86e97091041 MD5sum: 3ad25fc688fff35b82afb80ef9667383 Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd+1_all.deb Size: 10737524 SHA256: b6a893c9b80b40421f5d12d9a135bdc12fb17f3fab59e0106ef1fc24ad3e77af SHA1: 235814d62d21157760fbae3cf4401cc6d48cf555 MD5sum: 33cd04a3593f6f216ac68a3fc0ea82a4 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd+1_all.deb Size: 52180218 SHA256: 92b31d00b8ee13b7bcdf249cff509ec988cc2fc0703e301eed662571b89135f3 SHA1: 6540ca3feacd1d7efd53733b78fde41c7defb2c2 MD5sum: 973ce7224b20331a4ccfde26eba8acbe Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9273 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd+1_all.deb Size: 8991072 SHA256: a10f23addd5b16acad8feabb200ab8fda604f9ea177e7c32053ab0c60a768d9b SHA1: 65a715c185007eaccc71513dcad03da43d23cbdc MD5sum: 890e9a307f742f1573165ce80a18032f Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spyder Version: 2.2.5+dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 113 Depends: neurodebian-popularity-contest, python:any, python-spyderlib (= 2.2.5+dfsg-1~nd+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd+1_all.deb Size: 52982 SHA256: eddafac432df7d273ddf59837b6ff169aa3ec1776348d59dec5add709bdab935 SHA1: fb37bcc12d9ed07c06c79368f8da3b195323ecaf MD5sum: 96c4abda1f6c31f93c1b3b88f4771106 Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1122 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.2-1~nd+1_all.deb Size: 536902 SHA256: 60557533bb42657025adf9358290411f71d236569bda6eaa540228c4b4f217eb SHA1: 76bf900eb67fd567cb2615fa9b962b6069804907 MD5sum: a64cc8d4edfda4f4496468654d7b26d5 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: testkraut Version: 0.0.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd+1_all.deb Size: 100016 SHA256: 5ea3d436c473902040c138cb6100770fc3d0969e891ce84eabf2f644ee367a5a SHA1: b29570908455d37b7397bd9eed303b13e59b090b MD5sum: eafa9781d2a05681ca03188015f96302 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.6, 2.7 Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.3-1~nd+1_all.deb Size: 46989872 SHA256: 451a2f7f540a9cf2f8c276fbb575156845d0cd996733c38a7e11c5da17ac8ce8 SHA1: 18bac3730e0836a77d5f83cb380856e65a1e873b MD5sum: 87ceda4a59e22e74b68c7758551cb4b2 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit. Package: vtk-doc Source: vtk Version: 5.8.0-7+b0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 342007 Depends: neurodebian-popularity-contest, doc-base Suggests: libvtk5-dev, vtk-examples, vtkdata Homepage: http://www.vtk.org/ Priority: optional Section: doc Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd+1_all.deb Size: 66710216 SHA256: ef2921e37681f7364119b79457483cd3ca7da8cd063a96438cffe23aeba52938 SHA1: abc4b1ccf35fd6c0cc20f67836fb7ffcbfc69161 MD5sum: b7ef2d7972fe60ad7ce2f891faac4205 Description: VTK class reference documentation The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains exhaustive HTML documentation for the all the documented VTK C++ classes. The documentation was generated using doxygen and some excellent perl scripts from Sebastien Barre et. al. Please read the README.docs in /usr/share/doc/vtk-doc/ for details. The documentation is available under /usr/share/doc/vtk/html. Package: vtk-examples Source: vtk Version: 5.8.0-7+b0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2521 Depends: neurodebian-popularity-contest Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev Homepage: http://www.vtk.org/ Priority: optional Section: graphics Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd+1_all.deb Size: 578892 SHA256: fab181213376a1077411077e48a5640af76ceb2868302e2e03b18e4e6a0859fd SHA1: e0087beef829cbfd4d09abfd52a4e526b2b11963 MD5sum: efe0f5b35bccb7b5f9d251a25970a0ac Description: C++, Tcl and Python example programs/scripts for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains examples from the VTK source. To compile the C++ examples you will need to install the vtk-dev package as well. Some of them require the libqt4-dev package. . The Python and Tcl examples can be run with the corresponding packages (python-vtk, tcl-vtk).